@misc{10667, author = {{Hangmann, Hendrik}}, publisher = {{Paderborn University}}, title = {{{Generating Adjustable Temperature Gradients on modern FPGAs}}}, year = {{2012}}, } @article{10685, author = {{Kaufmann, Paul and Glette, Kyrre and Platzner, Marco and Torresen, Jim}}, journal = {{International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS)}}, number = {{4}}, pages = {{17--31}}, publisher = {{IGI Global}}, title = {{{Compensating Resource Fluctuations by Means of Evolvable Hardware: The Run-Time Reconfigurable Functional Unit Row Classifier Architecture}}}, doi = {{10.4018/jaras.2012100102}}, volume = {{3}}, year = {{2012}}, } @misc{10723, author = {{Platzner, Marco and Boschmann, Alexander and Kaufmann, Paul}}, pages = {{6--11}}, title = {{{Wieder natürlich gehen und greifen}}}, year = {{2012}}, } @misc{10734, author = {{Schmitz, Henning}}, publisher = {{Paderborn University}}, title = {{{Stereo Matching on a HC-1 Hybrid Core Computer}}}, year = {{2012}}, } @misc{10747, author = {{Topmöller, Christoph}}, publisher = {{Paderborn University}}, title = {{{Entwicklung eines Picoblaze Compilers mit dem Gentle Compiler Construction System}}}, year = {{2012}}, } @misc{10754, author = {{Wistuba, Martin}}, publisher = {{Paderborn University}}, title = {{{Analysis of Pattern Based Model Design and Learning in Computer-Go}}}, year = {{2012}}, } @inproceedings{11741, author = {{Chinaev, Aleksej and Haeb-Umbach, Reinhold}}, booktitle = {{Speech Communication; 10. ITG Symposium; Proceedings.}}, title = {{{Quality Analysis and Optimization of the MAP-based Noise Power Spectral Density Tracker}}}, year = {{2012}}, } @inproceedings{11745, abstract = {{In this paper we present a novel noise power spectral density tracking algorithm and its use in single-channel speech enhancement. It has the unique feature that it is able to track the noise statistics even if speech is dominant in a given time-frequency bin. As a consequence it can follow non-stationary noise superposed by speech, even in the critical case of rising noise power. The algorithm requires an initial estimate of the power spectrum of speech and is thus meant to be used as a postprocessor to a first speech enhancement stage. An experimental comparison with a state-of-the-art noise tracking algorithm demonstrates lower estimation errors under low SNR conditions and smaller fluctuations of the estimated values, resulting in improved speech quality as measured by PESQ scores.}}, author = {{Chinaev, Aleksej and Krueger, Alexander and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}}, booktitle = {{37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)}}, keywords = {{MAP parameter estimation, noise power estimation, speech enhancement}}, title = {{{Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor}}}, year = {{2012}}, } @inbook{11844, author = {{Krueger, Alexander and Haeb-Umbach, Reinhold}}, booktitle = {{Techniques for Noise Robustness in Automatic Speech Recognition}}, publisher = {{Wiley}}, title = {{{Reverberant Speech Recognition}}}, year = {{2012}}, } @inproceedings{11849, abstract = {{In this contribution we investigate the effectiveness of Bayesian feature enhancement (BFE) on a medium-sized recognition task containing real-world recordings of noisy reverberant speech. BFE employs a very coarse model of the acoustic impulse response (AIR) from the source to the microphone, which has been shown to be effective if the speech to be recognized has been generated by artificially convolving nonreverberant speech with a constant AIR. Here we demonstrate that the model is also appropriate to be used in feature enhancement of true recordings of noisy reverberant speech. On the Multi-Channel Wall Street Journal Audio Visual corpus (MC-WSJ-AV) the word error rate is cut in half to 41.9 percent compared to the ETSI Standard Front-End using as input the signal of a single distant microphone with a single recognition pass.}}, author = {{Krueger, Alexander and Walter, Oliver and Leutnant, Volker and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. Interspeech}}, title = {{{Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data}}}, year = {{2012}}, } @article{11863, abstract = {{In this contribution, a new observation model for the joint compensation of reverberation and noise in the logarithmic mel power spectral density domain will be considered. The proposed observation model relates the noisy reverberant feature to the underlying sequence of clean speech features and the feature of the noise. Nevertheless, due to the complex interaction of these variables in the target domain, the observationmodel cannot be applied to Bayesian feature enhancement directly, calling for approximations that eventually render the observation model useful. The performance of the approximated observation model will highly depend on the capability of modeling the difference between the model and the noisy reverberant observation. A detailed analysis of this observation error will be provided in this work. Among others, it will point out the need to account for the instantaneous ratio of the reverberant speech power and the noise power. Index Terms: Bayesian feature enhancement, observation model for noisy reverberant speech}}, author = {{Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}}, journal = {{Speech Communication; 10. ITG Symposium; Proceedings of}}, pages = {{1--4}}, title = {{{Investigations Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech}}}, year = {{2012}}, } @inproceedings{11864, abstract = {{In this work, an observation model for the joint compensation of noise and reverberation in the logarithmic mel power spectral density domain is considered. It relates the features of the noisy reverberant speech to those of the non-reverberant speech and the noise. In contrast to enhancement of features only corrupted by reverberation (reverberant features), enhancement of noisy reverberant features requires a more sophisticated model for the error introduced by the proposed observation model. In a first consideration, it will be shown that this error is highly dependent on the instantaneous ratio of the power of reverberant speech to the power of the noise and, moreover, sensitive to the phase between reverberant speech and noise in the short-time discrete Fourier domain. Afterwards, a statistically motivated approach will be presented allowing for the model of the observation error to be inferred from the error model previously used for the reverberation only case. Finally, the developed observation error model will be utilized in a Bayesian feature enhancement scheme, leading to improvements in word accuracy on the AURORA5 database.}}, author = {{Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}}, booktitle = {{Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference on}}, keywords = {{Robust Automatic Speech Recognition, Bayesian feature enhancement, observation model for reverberant and noisy speech}}, title = {{{A Statistical Observation Model For Noisy Reverberant Speech Features and its Application to Robust ASR}}}, year = {{2012}}, } @techreport{11865, author = {{Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}}, title = {{{Derivation of the Power Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral Domain}}}, year = {{2012}}, } @inproceedings{11910, author = {{Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}}, booktitle = {{International Workshop on Acoustic Signal Enhancement (IWAENC2012)}}, title = {{{Exploiting Temporal Correlations in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov Models}}}, year = {{2012}}, } @inproceedings{11998, author = {{Eckhoff, D. and Sommer, Christoph and German, R. and Dressler, F.}}, booktitle = {{2011 IEEE Global Telecommunications Conference - GLOBECOM 2011}}, isbn = {{9781424492688}}, title = {{{Cooperative Awareness at Low Vehicle Densities: How Parked Cars Can Help See through Buildings}}}, doi = {{10.1109/glocom.2011.6134402}}, year = {{2012}}, } @inproceedings{12001, author = {{Eckhoff, David and Sommer, Christoph and Dressler, Falko}}, booktitle = {{2012 IEEE 75th Vehicular Technology Conference (VTC Spring)}}, isbn = {{9781467309905}}, title = {{{On the Necessity of Accurate IEEE 802.11P Models for IVC Protocol Simulation}}}, doi = {{10.1109/vetecs.2012.6240064}}, year = {{2012}}, } @inproceedings{12021, author = {{Joerer, Stefan and Dressler, Falko and Sommer, Christoph}}, booktitle = {{Proceedings of the ninth ACM international workshop on Vehicular inter-networking, systems, and applications - VANET '12}}, isbn = {{9781450313179}}, title = {{{Comparing apples and oranges?}}}, doi = {{10.1145/2307888.2307895}}, year = {{2012}}, } @article{12023, author = {{Joerer, Stefan and Sommer, Christoph and Dressler, Falko}}, issn = {{0163-6804}}, journal = {{IEEE Communications Magazine}}, pages = {{82--88}}, title = {{{Toward reproducibility and comparability of IVC simulation studies: a literature survey}}}, doi = {{10.1109/mcom.2012.6316780}}, year = {{2012}}, } @inproceedings{12037, author = {{Malandrino, F. and Casetti, C. and Chiasserini, C. F. and Sommer, Christoph and Dressler, F.}}, booktitle = {{2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC)}}, isbn = {{9781467325691}}, title = {{{Content downloading in vehicular networks: Bringing parked cars into the picture}}}, doi = {{10.1109/pimrc.2012.6362591}}, year = {{2012}}, } @inbook{12895, author = {{Beutner, Marc and Pechuel, R.}}, booktitle = {{Jahrbuch eLearning und Wissensmanagement 2013. Die Zukunft der Bildung und die Rolle der digitalen Medien}}, editor = {{Siepmann, F and Müller, P}}, pages = {{30 -- 34}}, publisher = {{Siepmann Media}}, title = {{{mLearning. Akzeptanz von Mobile Learning. Chancen und Probleme in der betrieblichen Bildung}}}, year = {{2012}}, }